import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'DEVICE: {device}')
# data prarameters
# TODO: change the value of "concat_nframes" for medium baseline
concat_nframes = 25   # x the number of frames to concat with, n must be odd (total 2k+1 = n frames)
train_ratio = 0.75   # the ratio of data used for training, the rest will be used for validation

# training parameters
seed = 1213          # random seed
batch_size = 512        # batch size
num_epoch = 10         # the number of training epoch
learning_rate = 1e-4      # learning rate
model_path = './model.ckpt'  # the path where the checkpoint will be saved

# model parameters
# TODO: change the value of "hidden_layers" or "hidden_dim" for medium baseline
input_dim = 39 * concat_nframes  # the input dim of the model, you should not change the value
hidden_layers = 24          # the number of hidden layers
hidden_dim = 1024           # the hidden dim

